Using nested cross-validation, where 1/5 of the data was
used as evaluation data, and the remaining 4/5 were
repeatedly sub-sampled, 5 ensembles of 1200 neural networks
each were trained on HLA-A*02:02 peptide binding data. These
ensembles were shown to be good predictors of the evaluation
datasets, performing on par with the best networks in the
ensemble, illustrating the ''wisdom of the crowd'' principle. A
large dataset of peptides was analysed by the ensemble, and a
single neural network was trained on the predicted binding
scores. This condensed neural network was shown to have a
predictive performance slightly worse than the ensemble, but on
par with the ensemble from the online prediction server EasyPred. It thus
seems possible to train a single network containing at least some of the
''wisdom of the crowd'' of an ensemble.